docstr: Retrieve the weights of regressor features given the name
  
  Parameters
  ----------
      name : string
          Regressor name
  
  Returns
  -------
      torch.tensor
          Weight corresponding to the given regressor
docstr_tok: ["Retrieve","the","weights","of","regressor","features","given","the","name","Parameters","--","--","--","--","--","name",":","string","Regressor","name","Returns","--","--","--","-","torch.tensor","Weight","corresponding","to","the","given","regressor"]
code: def get_reg_weights(self, name):
      """
      Retrieve the weights of regressor features given the name
  
      Parameters
      ----------
          name : string
              Regressor name
  
      Returns
      -------
          torch.tensor
              Weight corresponding to the given regressor
      """
  
      regressor_dims = self.regressors_dims[name]
      mode = regressor_dims["mode"]
      index = regressor_dims["regressor_index"]
  
      if mode == "additive":
          regressor_params = self.regressor_params["additive"]
      else:
          assert mode == "multiplicative"
          regressor_params = self.regressor_params["multiplicative"]
  
      return regressor_params[:, index : (index + 1)]
  
file_path: neural_prophet/neuralprophet/components/future_regressors/linear.py
imports: import torch
  import torch.nn
  from neuralprophet.components.future_regressors import FutureRegressors
  from neuralprophet.utils_torch import init_parameter
label_desc: Use image node_id 3 for calling the LinearFutureRegressors obj's underlying member method code with example usage: obj.get_reg_weights(name) and returns: regressor_params